Promoting forest landscape dynamic prediction with an online collaborative strategy

Abstract

Modeling and predicting forest landscape dynamics are crucial for forest management and policy making, especially under the context of climate change and increased severities of disturbances. As forest landscapes change rapidly due to a variety of anthropogenic and natural factors, accurately and efficiently predicting forest dynamics requires the collaboration and synthesis of domain knowledge and experience from geographically dispersed experts. Owing to advanced web techniques, such collaboration can now be achieved to a certain extent, for example, discussion about modeling methods, consultation for model use, and surveying for stakeholders' feedback can be conducted on the web. However, a research gap remains in terms of how to facilitate online joint actions in the core task of forest landscape modeling by overcoming the challenges from decentralized and heterogeneous data, offline model computation modes, complex simulation scenarios, and exploratory modeling processes. Therefore, we propose an online collaborative strategy to enable collaborative forest landscape dynamic prediction with four core modules, namely data preparation, forest landscape model (FLM) computation, simulation scenario configuration, and process organization. These four modules are designed to support: (1) voluntary data collection and online processing, (2) online synchronous use of FLMs, (3) collaborative simulation scenario design, altering, and execution, and (4) participatory modeling process customization and coordination. We used the LANDIS-II model as a representative FLM to demonstrate the online collaborative strategy for predicting the dynamics of forest aboveground biomass. The results showed that the online collaboration strategy effectively promoted forest landscape dynamic prediction in data preparation, scenario configuration, and task arrangement, thus supporting forest-related decision making.

Keywords: Collaborative framework; Forest landscape modeling; LANDIS-II; OpenGMS; Web-based prediction.